1. Technical Field
The present invention relates to a technology for automatically segmenting an image corresponding to a joint from a skeletal medial image.
2. Description of the Related Art
A joint is an important portion of the human body at which bones are connected, and acts as the axis for various types of exercises of the spine and the limbs to enable various exercises. However, when a bone forming a joint portion is fractured due to aging or osteoporosis, it makes difficult an independent movement and may lead to death in severe cases.
In particular, in accordance with data examined by “Health Insurance Review & Assessment Service”, the death rate within one year after a hip joint fracture in people of over 50 years old in Korea was 14.8% in women and 20.9% in men, which are very high levels. Furthermore, due to the ageing society, the number of hip joint fracture patients steadily increases, and a corresponding social cost also greatly increases over 1 trillion won or more.
Active research is recently carried on a danger assessment technology regarding osteoporosis early diagnosis and osteoporosis fracture with the development of medical imaging devices, image processing schemes and medical engineering field. A method of assessing the bone strength of a patient in association with a 3D quantitative computed tomography image and finite element analysis has recently been in the spotlight by the academic world and medical system because the structural strength of a target skeletal system can be quantitatively estimated based on a bone density distribution of an individual patient. For the bone strength assessment based on finite element method (FEM), an image segmentation technology for segmenting only a joint portion from complicated medical image information formed of muscle, bones, etc. and configuring a finite element model must be secured.
Due to the morphological characteristics of a joint and the technological limit of a medical imaging device, automatic image segmentation for a joint portion is still in the early stage. In general, a joint has a low signal-to-noise ratio (SNR) because it is located in the deep place of the body. A thin cortical bone of the condyle and the articular cartilage are vulnerable to a partial volume effect according to low spatial resolution of an in-vivo medical imaging device. Due to such a problem, the existing joint image segmentation has been performed according to a manual or semi-automatic method that requires a user's intervention.
If an expert uses the corresponding technology, a joint image can be segmented, but there are problems, such as long working hours and different results according to users. Accordingly, a lot of search is recently carried out to develop a joint automation image segmentation method having high accuracy and reliability and a short processing time. The method of automatically segmenting an image is basically divided into an unsupervised approach and a supervised approach.
The unsupervised approach is a method of segmenting a joint using only an image processing scheme without prior information about a joint, and it has advantages of implementation convenience and a short processing time, but does not guarantee reliable joint segmentation. For example, the unsupervised approach includes a thresholding method, a region growing method and a watershed algorithm.
The thresholding method is a method of removing a predetermined threshold or less from an image and leaving a threshold or more in the image. This method consumes very little time and is the simplest method, but it is difficult to obtain region information of a shape because connectivity between segmented images is not guaranteed.
The region growing method is a method of gradually expanding to a region having a similar image element value using a seed point as a starting point. This method can provide region information of a shape because connectivity between segmented images is guaranteed, but it obtains erroneous results because an unwanted region is extended if a comparison between image elements is small.
The watershed algorithm is a method of binding morphologically similar regions in a patch form while filling an individual valley with water by considering an input image as a geographical structure. In this method, the morphological characteristics of a structure may be considered, but it is not easy to select a patch of a desired form without region information because this method tends to excessively segment an image.
The supervised approach is a method of automatically segmenting a joint using accumulated shape information of joints, but it is not easy to construct a plurality of pieces of image information because the results of segmentation depends on accumulated image information. Representative examples of the supervised approach include a statistical shape model-based method and an atlas-based method. The statistical shape model-based method is a method of statistically representing a target shape using a plurality of pieces of prior information, searching for the landmark of the target shape when a specific image is received, representing the image using a statistical model, and then performing image segmentation. In this method, image segmentation is relatively well performed if a target shape is similar to prior information, but image segmentation fails if an image different from prior information is received.
The atlas-based method is a method of constructing an atlas, that is, a plurality of shape models, by considering the size, direction and form of a target shape and using the atlas for image segmentation. This method is a method of selecting an atlas having the smallest error by comparing a specific input image with the existing atlas. The atlas-based method can obtain precise results for a predictable shape, but fails in image segmentation if there is an element that has not been previously considered.
Accordingly, there is a need for a technology for fully automatically segmenting an image of a joint portion from a skeletal medial image.
Korean Patent Publication No. 10-2017-0000040relates to a medical image segmentation apparatus and method based on user input that may be fed back, and discloses a technology for generating a 2D target segmentation image, including an interested region to be segmented and background region selected by a user, from a displayed 3D medical image and extending the 2D target segmentation image in the 3D manner.